State College, PA, United States of America

Dongsheng Luo

This inventor holds 1 USPTO granted patent and 5 published patent applications. Top assignee: Nec Corporation. Active years: 2026.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Innovations of Dongsheng Luo

Introduction

Dongsheng Luo is an accomplished inventor based in State College, PA (US). He has made significant contributions to the field of computer science, particularly in the area of time series representation learning. His innovative approach combines meta-learning techniques to enhance the efficiency of time series analysis.

Latest Patents

Dongsheng Luo holds a patent titled "Contrastive time series representation learning via meta-learning." This patent describes a computer-implemented method that includes receiving a training time series along with corresponding labels. The method optimizes time series augmentations through a selection process performed by a meta learner. This results in a selected augmentation from various candidate augmentations. The training of a time series encoder with contrastive loss is then conducted using the selected augmentation, leading to the development of a learned time series encoder. This encoder is capable of learning vector representations of other time series and performing downstream tasks such as label classification.

Career Highlights

Dongsheng Luo is currently associated with NEC Corporation, where he applies his expertise in innovative technologies. His work focuses on advancing methodologies that improve data analysis and machine learning processes.

Collaborations

He collaborates with notable colleagues, including Haifeng Chen and Wei Cheng, who contribute to his research and development efforts.

Conclusion

Dongsheng Luo's contributions to time series representation learning exemplify the impact of innovative thinking in technology. His work continues to influence advancements in machine learning and data analysis.

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